Results 251 to 260 of about 11,564,802 (285)

Variational learning for Dirichlet process mixtures of Dirichlet distributions and applications

Multimedia Tools and Applications, 2012
In this paper, we propose a Bayesian nonparametric approach for modeling and selection based on a mixture of Dirichlet processes with Dirichlet distributions, which can also be seen as an infinite Dirichlet mixture model. The proposed model uses a stick-breaking representation and is learned by a variational inference method.
Wentao Fan 0001, Nizar Bouguila
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A Dirichlet process mixture of dirichlet distributions for classification and prediction

2008 IEEE Workshop on Machine Learning for Signal Processing, 2008
A significant problem in clustering is the determination of the number of classes which best describes the data. This paper proposes a learning approach based on both Dirichlet process and Dirichlet distribution which provide flexible nonparametric Bayesian framework for non-Gaussian data clustering.
Nizar Bouguila, Djemel Ziou
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Maximum Margin Dirichlet Process Mixtures for Clustering

Proceedings of the AAAI Conference on Artificial Intelligence, 2016
The Dirichlet process mixtures (DPM) can automatically infer the model complexity from data. Hence it has attracted significant attention recently, and is widely used for model selection and clustering. As a generative model, it generally requires prior base distribution to learn component parameters by maximizing posterior probability.
Gang Chen 0032, Haiying Zhang, Caiming Xiong   +2 more
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Online Variational Learning of Dirichlet Process Mixtures of Scaled Dirichlet Distributions

Information Systems Frontiers, 2020
Data clustering as an unsupervised method has been one of the main attention-grabbing techniques and a large class of tasks can be formulated by this method. Mixture models as a branch of clustering methods have been used in various fields of research such as computer vision and pattern recognition.
Narges Manouchehri, Hieu Nguyen, Pantea Koochemeshkian, Nizar Bouguila, Wentao Fan 0001   +4 more
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Applications of Dirichlet Process Mixtures to speaker adaptation

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012
Balancing unique acoustic characteristics of a speaker such as identity and accent, with general acoustic behavior that describes phoneme identity, is one of the great challenges in applying nonparametric Bayesian approaches to speaker adaptation. The Dirichlet Process Mixture (DPM) is a relatively new model that provides an elegant framework in which ...
Amir Hossein Harati Nejad Torbati, Joe Picone, Marc Sobel   +2 more
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Dirichlet Process Mixture of Mixtures Model for Unsupervised Subword Modeling

IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018
We develop a parallelizable Markov chain Monte Carlo sampler for a Dirichlet process mixture of mixtures model. Our sampler jointly infers a codebook and clusters. The codebook is a global collection of components. Clusters are mixtures, defined over the codebook.
Michael Heck, Sakriani Sakti, Satoshi Nakamura 0001   +2 more
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A hierarchical Dirichlet process mixture of generalized Dirichlet distributions for feature selection

Computers & Electrical Engineering, 2015
Display Omitted A statistical framework based on hierarchical Dirichlet processes and generalized Dirichlet distribution is developed.The framework simultaneously performs model parameters estimations as well as model complexity determination.The learning of the model is done via variational Bayes inference.The efficiency of the proposed algorithm is ...
Wentao Fan 0001, Hassen Sallay, Nizar Bouguila, Sami Bourouis   +3 more
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A Probability for Classification Based on the Dirichlet Process Mixture Model

Journal of Classification, 2010
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Ruth Fuentes-García, Ramsés H. Mena, Stephen G. Walker   +2 more
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Temporally-Reweighted Dirichlet Process Mixture Anomaly Detector

2020 International Conference on Data Mining Workshops (ICDMW), 2020
This paper proposes a streaming anomaly detection algorithm using variational Bayesian non-parametric methods. We extend the use of Dirichlet process mixture models to anomaly detection for online streaming data through the use of streaming variational bayes method and a cohesion function.
JunYong Tong, Nick Torenvliet
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Estimating Mixture of Dirichlet Process Models

Journal of Computational and Graphical Statistics, 1998
Abstract Current Gibbs sampling schemes in mixture of Dirichlet process (MDP) models are restricted to using “conjugate” base measures that allow analytic evaluation of the transition probabilities when resampling configurations, or alternatively need to rely on approximate numeric evaluations of some transition probabilities.
Steven N. Maceachern, Peter Müller
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